U.S. patent number 6,699,194 [Application Number 09/547,588] was granted by the patent office on 2004-03-02 for signal processing apparatus and method.
This patent grant is currently assigned to Masimo Corporation. Invention is credited to Mohamed K. Diab, Rex McCarthy.
United States Patent |
6,699,194 |
Diab , et al. |
March 2, 2004 |
**Please see images for:
( Reexamination Certificate ) ** |
Signal processing apparatus and method
Abstract
A method and an apparatus to analyze two measured signals that
are modeled as containing desired and undesired portions such as
noise, FM and AM modulation. Coefficients relate the two signals
according to a model defined in accordance with the present
invention. In one embodiment, a transformation is used to evaluate
a ratio of the two measured signals in order to find appropriate
coefficients. The measured signals are then fed into a signal
scrubber which uses the coefficients to remove the unwanted
portions. The signal scrubbing is performed in either the time
domain or in the frequency domain. The method and apparatus are
particularly advantageous to blood oximetry and pulserate
measurements. In another embodiment, an estimate of the pulserate
is obtained by applying a set of rules to a spectral transform of
the scrubbed signal. In another embodiment, an estimate of the
pulserate is obtained by transforming the scrubbed signal from a
first spectral domain into a second spectral domain. The pulserate
is found by identifying the largest spectral peak in the second
spectral domain.
Inventors: |
Diab; Mohamed K. (Mission
Viejo, CA), McCarthy; Rex (Mission Viejo, CA) |
Assignee: |
Masimo Corporation (Irvine,
CA)
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Family
ID: |
25266342 |
Appl.
No.: |
09/547,588 |
Filed: |
April 11, 2000 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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081539 |
May 19, 1998 |
6067462 |
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834194 |
Apr 14, 1997 |
6002952 |
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Current U.S.
Class: |
600/481 |
Current CPC
Class: |
A61B
5/02416 (20130101); A61B 5/02433 (20130101); A61B
5/14551 (20130101); A61B 5/7214 (20130101); A61B
5/7257 (20130101); G06K 9/0051 (20130101); A61B
5/0205 (20130101); A61B 5/14552 (20130101); A61B
5/7203 (20130101); A61B 5/6826 (20130101); A61B
5/7278 (20130101); A61B 5/7225 (20130101); A61B
5/742 (20130101); A61B 5/1455 (20130101); A61B
5/726 (20130101) |
Current International
Class: |
A61B
5/02 (20060101); A61B 005/02 () |
Field of
Search: |
;600/300,310,322,323,324,336,481,508,509,515 ;702/191-197 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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0102816 |
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Mar 1984 |
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EP |
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0329196 |
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Aug 1989 |
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EP |
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0743042 |
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Nov 1996 |
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EP |
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0744154 |
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Nov 1996 |
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EP |
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WO9409698 |
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May 1994 |
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WO |
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Other References
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Canceling", Adaptive Signal Processing, Prentice-Hall, Inc., pp.
302-367, 1985. .
Tremper, Kevin K. et al., "Pulse Oximetry: Technical Aspects of
Machine Design" by Jonas A. Pologe, Advances in Oxygen Monitoring,
Little, Brown and Company, 1987, pp. 137-153. .
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Dobb's Journal, Jan. 1989, pp. 32-34, 36-38, 42, 47, 49, 96, 98,
100. .
Neuman, Michael R., "Pulse Oximetry: Physical Principles, Technical
Realization and Present Limitations", Continuous Transcutaneous
Monitoring, Plenum Press, New York, 1987, pp. 135-144. .
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Biomedical Signal Processing, CRC Press, Inc., pp. 152-159. .
Harris, Fred et al., "Digital Signal Processing With Efficient
Polyphase Recursive All-Pass Filters" Article, Presented at
International Conference on Signal Processing Florence, Italy, Sep.
4-6, 1991. .
Rabiner, Lawrence R. et al., "Theory and Approximation of Infinite
Impulse Response Digital Filters", Prentice Hall Inc., Englewood
Cliffs, NJ, 1975, p. 260. .
Mook, G.A. et al., "Spectrophotometric determination of oxygen
saturation of blood independent of the presence of indocyanine
green", Cardiovascular Research, vol. 13, 1979, pp. 233-237. .
Melnikof, Steve, "Neural Networks for Signal Processing: A Case
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Mook, G.A. et al., "Wavelength dependency of the spectrophotometric
determination of blood oxygen saturation", Clinical Chemistry Acta,
vol. 26, 1969, pp. 170-173. .
Brown, David P., "Evaluation of Pulse Oximeters using Theoretical
Models and Experimental Studies" Masters Thesis, University of
Washington, 1987. .
Haykin, Simon, Adaptive Filter Theory, Prentice Hall, Englewood
Cliffs, NJ, Chapter 9, "Recursive Least-Squares Lattice Filters,"
pp. 451-493, 1985. .
Widrow, Bernard, Adaptive Signal Processing, Prentice Hall,
Englewood Cliffs, NJ, Chapter 1, "Adaptive Systems," pp. 3-13,
1985..
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Primary Examiner: Winakur; Eric F.
Attorney, Agent or Firm: Knobbe, Martens, Olson & Bear
LLP
Parent Case Text
This application is a continuation of prior application Ser. No.
09/081,539 filed May 19, 1998, now U.S. Pat. No. 6,067,462, which
is a divisional of application Ser. No. 08/834,194 filed Apr. 14,
1997 now U.S. Pat. No. 6,002,
Claims
What is claimed is:
1. A physiological monitor for monitoring a pulserate of a living
being, said monitor comprising a signal processor configured to:
transform a time-domain plethysmograph dataset into a
spectral-domain dataset; identify three or more spectral peaks at
non-zero frequencies in said spectral domain dataset; and sort said
three or more spectral peaks according to one or more rules into
one or more spectral peaks corresponding to a fundamental frequency
and one or more harmonics of said fundamental frequency, and
estimate a pulserate from said fundamental frequency and said one
or more harmonics.
2. The physiological monitor of claim 1, wherein said one or more
rules comprise comparisons of relative magnitudes of one or more of
said spectral peaks.
3. In a physiological monitor for measuring a pulserate of a living
being, said monitor having a detector producing a detector output
waveform corresponding to a time-domain plethysmograph waveform, a
method comprising: transforming a time-domain plethysmograph
waveform into a spectral domain waveform; identifying three or more
spectral peaks at non-zero frequencies in said spectral domain
waveform; classifying said three or more spectral peaks into a
first group comprising one or more spectral peaks corresponding to
a fundamental frequency and a second group comprising one or more
harmonics of said fundamental frequency; and estimating a pulserate
from at least said first group.
4. In a physiological monitor for measuring a pulserate of a living
being, said monitor having a detector producing a detector output
waveform corresponding to a time-domain plethysmograph waveform, a
method comprising: transforming a time-domain plethysmograph
waveform into a spectral domain waveform; identifying a plurality
of spectral peaks in said spectral domain waveform; classifying
said plurality of spectral peaks into a first group comprising one
or more spectral peaks corresponding to a fundamental frequency and
a second group comprising one or more harmonics of said fundamental
frequency; and estimating a pulserate from at least said first
group, wherein said plurality of spectral peaks are classified
according to ratios.
5. In a physiological monitor for measuring a pulserate of a living
being, said monitor having a detector producing a detector output
waveform corresponding to a time-domain plethysmograph waveform, a
method comprising: transforming a time-domain plethysmograph
waveform into a spectral domain waveform; identifying a plurality
of spectral peaks in said spectral domain waveform; classifying
said plurality of spectral peaks into a first group comprising one
or more spectral peaks corresponding to a fundamental frequency and
a second group comprising one or more harmonics of said fundamental
frequency, and estimating a pulserate from at least said first
group, wherein said spectral domain waveform comprises a first
component corresponding to a first frequency of light passed
through a portion of said living being, and a second component
corresponding to a second frequency of light passed through a
portion of said living being, and wherein said plurality of
spectral peaks are classified at least in part according to one or
more ratios, said one or more ratios corresponding to ratios of at
least one or more portions of said first component with at least
one or more portions of said second component.
6. The method of claim 5, wherein said ratios are classified
according to one or more rules.
7. An apparatus for monitoring physiological parameters of a living
organism having a pulserate, said apparatus comprising: means for
producing a time-domain plethysmograph waveform; means for
transforming said time-domain plethysmograph waveform into a
spectral domain waveform having a fundamental spectral peak
corresponding to said pulserate and two or more ancillary spectral
peaks, and classifying said fundamental spectral peak and said
ancillary spectral peaks to estimate said pulserate.
8. A physiological monitor for monitoring a living being having a
pulserate, said monitor comprising a signal processor configured
to: transform a time-domain plethysmograph dataset into a
spectral-domain dataset; classify spectral lines in said
spectral-domain dataset into a group of spectral values
corresponding to a fundamental and oft two or more harmonics of
said fundamental; and estimate a pulserate from said group of
spectral values according to one or more rules.
9. A physiological monitor comprising a signal processor configured
to: transform a time-domain plethysmograph dataset into a
spectral-domain dataset; classify at least three spectral lines in
said spectral-domain dataset into a group of spectral values
corresponding to a first group of one or more spectral lines and at
least one second group of spectral lines, said second group of
spectral lines comprising at least one harmonic of said first
group; and estimate said pulserate from said first group and at
least one of said second group.
10. In a physiological monitor to monitor pulserate, said monitor
having a detector responsive to physiological properties related to
light passed through a living being, a method comprising:
transforming a first time-domain plethysmograph waveform into a
first spectral domain waveform, said first time-domain
plethysmograph waveform corresponding to a first frequency of light
passed through a living being; transforming a second time-domain
plethysmograph waveform into a second spectral domain waveform,
said second time-domain plethysmograph waveform corresponding to a
second frequency of light passed through said living being;
classify one or more spectral values obtained from a ratio of said
first spectral domain waveform and said second spectral domain
waveform into a series of spectral peaks comprising a fundamental
peak and at least one harmonics of said fundamental peak; and
estimating said pulserate from said series of spectral peaks.
11. The method of claim 10, wherein said pulserate is estimated
according to a center of mass of at least a portion of said series
of spectral peaks.
12. The method of claim 10, wherein an estimate of said pulserate
is associated with a confidence factor.
13. The method of claim 10, further comprising computing a
confidence factor indicating a likelihood that an estimate for the
pulserate represents the actual pulserate of the living being.
14. In a physiological monitor attached to a living organism having
a pulserate, said monitor having a detector responsive to
physiological properties related to pulserate, a method comprising
the steps of: transforming a time-domain plethysmograph waveform
into a spectral domain waveform; classifying one or more spectral
values obtained from said spectral domain waveform; and using
results from a center of mass calculation of at least a portion of
said spectral values to estimate said pulserate.
15. A physiological monitor for monitoring comprising a signal
processor configured to: transform a time-domain representation of
a plethysmograph waveform into a spectral-domain representation of
said plethysmograph waveform, said spectral-domain representation
having at least three spectral peaks at non-zero frequencies;
select a selected portion of said spectral-domain representation
based on one or more rules relating to characteristics of spectral
lines in said selected portion and one or more harmonics of
spectral lines in said selected portion; and estimate said
pulserate from said selected portion of said spectral-domain
representation.
16. A physiological monitor comprising a signal processor
configured to: transform a first time-domain representation of a
first plethysmograph waveform corresponding to a first optical
measurement wavelength into a spectral domain to produce a first
spectral-domain representation representing said first
plethysmograph waveform and transform a second time-domain
representation of a second plethysmograph waveform corresponding to
a second optical measurement wavelength into said spectral domain
to produce a second spectral-domain representation representing
said second plethysmograph waveform; classify spectral data from
said first spectral-domain representation and said second
spectral-domain representation at least in part according to a
ratio of at least a portion of said first spectral-domain
representation and at least a portion of said second
spectral-domain representation to identify a series of spectral
peaks corresponding to a fundamental and at least one or more
harmonics of said fundamental; and estimate said pulserate from
said series of spectral peaks as a function of a center of mass
type of calculation of at least a portion of said series of
spectral peaks.
17. A physiological monitor comprising a signal processor
configured to: transform a first time-domain representation of a
first plethysmograph waveform corresponding to a first optical
measurement wavelength into a spectral domain to produce a first
spectral-domain representation representing said first
plethysmograph waveform and a second time-domain representation of
a second plethysmograph waveform corresponding to a second optical
measurement wavelength into said spectral domain to produce a
second spectral-domain representation representing said second
plethysmograph waveform; classify spectral data from said first
spectral-domain representation and said second spectral-domain
representation at least in part according to a ratio of at least a
portion of said first spectral-domain representation and at least a
portion of said second spectral-domain representation to identify a
series of spectral peaks corresponding to a fundamental and at
least one or more additional spectral peaks at frequencies higher
than said fundamental; and compute an estimate of said pulserate
from said series of spectral peaks according to said fundamental
and at least one of said one or more additional spectral peaks.
18. The physiological monitor of claim 17, said signal processor
further configured to calculate a confidence factor related to said
estimate of said pulserate.
19. The physiological monitor of claim 17, wherein said series of
spectral peaks are classified using one or more rules.
20. The physiological monitor of claim 17, wherein said series of
spectral peaks are classified using one or more rules, said one or
more rules comprising comparisons of relative magnitudes of one or
more of said spectral peaks.
21. The physiological monitor of claim 17, wherein said series of
spectral peaks are classified using one or more rules related to
said ratio.
22. The physiological monitor of claim 17, wherein said series of
spectral peaks are classified using one or more rules related to
said ratio and frequencies of said spectral peaks.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to the field of signal processing.
More specifically, the: present invention relates to the processing
of measured signals, containing a primary signal portion and a
secondary signal portion, for the removal or derivation of either
the primary or secondary signal portion when little is known about
either of these components. The present invention is especially
useful for physiological monitoring systems including blood oxygen
saturation systems and pulserate measurement systems. The present
invention further relates to a method and apparatus for signal
processing of signals in order to compute an estimate for
pulserate.
2. Description of the Related Art
Signal processors are typically employed to remove or derive either
the primary or secondary, signal portion from a composite measured
signal including a primary signal portion and a secondary signal
portion. For example, a composite signal may contain a primary
signal portion comprising desirable data and a secondary signal
portion comprising noise. If the secondary signal portion occupies
a different frequency spectrum than the primary signal portion,
then conventional filtering techniques such as low pass, band pass,
and high pass filtering are available to remove or derive either
the primary or the secondary signal portion from the total signal.
Fixed single or multiple notch filters could also be employed if at
least one of the primary and secondary signal portions exists at a
fixed frequency band.
It is often the case that an overlap in frequency spectrum between
the primary and secondary signal portions exists. Complicating
matters further, the statistical properties of one or both of the
primary and secondary signal portions may change with time. In such
cases, conventional filtering techniques are ineffective in
extracting either the primary or secondary signal. If, however, a
description of either the primary or secondary signal portion can
be derived, correlation canceling, such as adaptive noise
canceling, can be employed to remove either the primary or
secondary signal portion of the signal isolating the other portion.
In other words, given sufficient information about one of the
signal portions, that signal portion can be extracted.
Conventional correlation cancelers, such as adaptive noise
cancelers, dynamically change their transfer function to adapt to
and remove portions of a composite signal. However, correlation
cancelers and adaptive noise cancelers require either a secondary
reference or a primary reference which correlates to either the
secondary signal portion only or the primary signal portion only.
For instance, for a measured signal containing noise and desirable
signal, the noise can be removed with a correlation canceler if a
noise reference is available. This is often the case. Although the
amplitudes of the reference signals are not necessarily the same as
the amplitudes of the corresponding primary or secondary signal
portions, the reference signals have a frequency spectrum which is
similar to that of the primary or secondary signal portions.
In many cases, nothing, or very little is known about the secondary
and primary signal portions. One area where measured signals
comprising a primary signal portion and a secondary signal portion
about which no information can easily be determined is
physiological monitoring. Physiological monitoring generally
involves measured signals derived from a physiological system, such
as the human body. Measurements which are typically taken with
physiological monitoring systems include electrocardiographs, blood
pressure, blood gas saturation (such as oxygen saturation),
capnographs, other blood constituent monitoring, heart rate,
respiration rate, electro-encephalograph (EEG) and depth of
anesthesia, for example. Other types of measurements include those
which measure the pressure and quantity of a substance within the
body such as cardiac output, venous oxygen saturation, arterial
oxygen saturation, bilirubin, total hemoglobin, breathalyzer
testing, drug testing, cholesterol testing, glucose testing, and
carbon dioxide testing, protein testing, carbon monoxide testing,
and other in-vivo measurements, for example. Complications arising
in these measurements are often due to motion of the patient, both
external and internal (muscle movement, vessel movement, and probe
movement, for example), during the measurement process.
Many types of physiological measurements can be made by using the
known properties of energy attenuation as a selected form of energy
passes through a test medium such as a finger, shown schematically
in FIG. 1.
A blood gas monitor is one example of a physiological monitoring
system which is based upon the measurement of energy attenuated by
biological tissues or substances. Blood gas monitors transmit light
into the test medium and measure the attenuation of the light as a
function of time. The output signal of a blood gas monitor which is
sensitive to the arterial blood flow contains a component having a
waveform representative of the patient's arterial pulse. This type
of signal, which contains a component related to the patient's
pulse, is called a plethysmographic wave, and is shown in FIG. 2A
as a curve s(t) 201. Plethysmographic waveforms are used in blood
gas saturation measurements. As the heart beats, the amount of
blood in the arteries increases and decreases, causing increases
and decreases in energy attenuation, illustrated by a cyclic wave
seen in the curve 201.
Typically, a digit such as a finger, an ear lobe, or other portion
of the body where blood flows close to the skin, is employed as the
medium through which light energy is transmitted for blood gas
attenuation measurements. The finger comprises skin, fat, bone,
muscle, etc., as shown FIG. 1, each of which attenuates energy
incident on the finger in a generally predictable and constant
manner. However, when fleshy portions of the finger are compressed
erratically, for example by motion of the finger, energy
attenuation becomes erratic.
An example of a more realistic measured waveform is shown in FIG.
2B, as a curve M(t) 202. The curve 202 illustrates the effect of
motion and noise n(t) added to the clean waveform s(t) shown in
FIG. 201. The primary plethysmographic waveform portion of the
signal M(t) is the waveform representative of the pulse,
corresponding to the sawtooth-like pattern wave in curve 201. The
large, secondary motion-induced excursions in signal amplitude
obscure the primary plethysmographic signal s(t). Even small
variations in amplitude make it difficult to distinguish the
primary signal component s(t) in the presence of a secondary signal
component n(t).
A pulse oximeter is a type of blood gas monitor which
non-invasively measures the arterial saturation of oxygen in the
blood. The pumping of the heart forces freshly oxygenated blood
into the arteries causing greater energy attenuation. As well
understood in the art, the arterial saturation of oxygenated blood
may be determined from the depth of the valleys relative to the
peaks of two plethysmographic waveforms measured at separate
wavelengths. Patient movement introduces motion artifacts to the
composite signal as illustrated in the plethysmographic waveform
illustrated in FIG. 2B. These motion artifacts distort the measured
signal.
SUMMARY OF THE INVENTION
The present invention involves several different embodiments using
the novel signal model in accordance with the present invention to
estimate the desired signal portion of a measured data signal where
the measured data contains desired and undesired components. In one
embodiment, a signal processor acquires a first measured signal and
a second measured signal. The first signal comprises a desired
signal portion and an undesired signal portion. The second measured
signal comprises a desired signal portion and an undesired signal
portion. The signals may be acquired by propagating energy through
a medium and measuring an attenuated signal after transmission or
reflection. Alternatively, the signals may be acquired by measuring
energy generated by the medium.
In one embodiment, the desired signal portions of the first and
second measured signals are approximately equal to one another, to
with a first constant multiplier. The undesired signal portions of
the first and second measured signals are also approximately equal
to one another, to within a second constant multiplier. A scrubber
coefficient may be determined, such that an estimate for the first
signal can be generated by inputting the first and second measured
signals, and the scrubber coefficient into a waveform scrubber. The
output of the waveform scrubber is generated by multiplying the
first measured signal by the scrubber coefficient and then adding
the result to the second measured signal.
In one embodiment, the scrubber coefficient is determined by
normalizing the first and second measured signals, and then
transforming the normalized signals into a spectral domain. The
spectral domain signals are then divided by one another to produce
a series of spectral ratio lines. The need for waveform scrubbing
can be determined by comparing the largest ratio line to the
smallest ratio line. If the difference does not exceed a threshold
value, the no scrubbing is needed. If the difference does exceed a
threshold value, then the waveform must be scrubbed, and the
scrubbing coefficient corresponds to the magnitude of the largest
ratio line.
Another aspect of the present invention involves a physiological
monitor having a signal processor which computes an estimate for an
unknown pulserate from the measured data. In one embodiment, the
signal processor receives measured data from a detector that
measures a physiological property related to the heartbeat. The
signal processor transforms the data into a spectral domain and
then identifies a series of spectral peaks and the frequencies
associated with those peaks. The signal processor then applies a
set of rules to the spectral peaks and the associated frequencies
in order to compute an estimate for the pulserate.
In yet another embodiment of the pulserate detector, the signal
processor performs a first transform to transform the measured data
into a first transform space. The signal processor then performs a
second transform to transform the data from the first transform
space into a second transform space. The signal processor then
searches the data in the second transform space to find the
pulserate.
In another embodiment, the transform into the first transform space
is a spectral transform such as a Fourier transform. In another
embodiment, the transform into the second transform space is a
spectral transform such as a Fourier transform. In yet another
embodiment, once the data has been transformed into the second
transform space, the signal processor performs a 1/x mapping on the
spectral coordinates before searching for the pulserate.
In another embodiment, the signal processor transforms the measured
data into a first spectral domain, and then transforms the data
from the first spectral domain into a second spectral domain. After
twice transforming the data, the signal processor performs a 1/x
remapping on the coordinates of the second spectral domain. The
signal processor then searches the remapped data for the largest
spectral peak corresponding to a pulserate less than 120 beats per
minute. If such a peak is found, then the signal processor outputs
the frequency corresponding to that peak as being the pulserate.
Otherwise, the signal processor searches the data transformed into
the first spectral domain for the largest spectral peak in that
domain, and outputs a pulserate corresponding to the frequency of
the largest peak in the first spectral domain.
In another embodiment of the pulserate detector, the signal
processor first transforms the measured data into a first spectral
domain. Then the signal processor takes the magnitude of the
transformed data and then transforms the magnitudes into a second
spectral domain. Then the signal processor then performs a 1/x
mapping of the spectral coordinates. After the 1/x mapping, the
signal processor feeds the transformed and remapped data into a
neural network. The output of the neural network is the
pulserate.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 schematically illustrates a typical finger.
FIG. 2A illustrates an ideal plethysmographic waveform.
FIG. 2B illustrates a plethysmographic waveform which includes a
motion-induced erratic signal portion.
FIG. 3 illustrates a schematic diagram of a physiological monitor
in accordance with the teachings of one aspect of the present
invention
FIG. 4 illustrates an example of a low noise emitter current driver
with accompanying digital to analog converter in accordance with
the teachings of one aspect of the present invention.
FIG. 5 illustrates the absorption properties of hemoglobin at
various wavelength.
FIG. 6 illustrates one cycle of an idealized plethysmographic
waveform for various levels of oxygen saturation at a fixed
perfusion.
FIG. 7 illustrates a block diagram of the signal processing used to
compute the ratio of red signal to infrared signal in accordance
with one aspect of the present invention.
FIG. 8 is a graph which illustrates the relationship between the
red/infrared ratio and blood oxygen saturation.
FIG. 9 is a graph which illustrates the relationship between the
ideal red and infrared signals, and the relationship between
measured red and infrared signals.
FIG. 10 illustrates a model for measured data in a pulse
oximeter.
FIG. 11 is an idealized frequency domain plot of the red and
infrared transmission signals
FIG. 12 is a block diagram of a motion detector and removal system
in accordance with one aspect of the present invention.
FIG. 13 is a flowchart showing the processing steps of a motion
detector and removal method in accordance with one aspect of the
present invention.
FIG. 14A is an idealized frequency domain plot of an
plethysmographic wave.
FIG. 14B is an idealized frequency domain plot of a
plethysmographic wave showing the effect of FM modulation.
FIG. 14C is an idealized time domain plot of a superimposed pair of
plethysmographic waves that can be used to model an FM modulated
plethysmographic wave.
FIG. 15 is an idealized frequency domain plot of a plethysmographic
wave showing the effects of AM modulation.
FIG. 16 is a group of idealized frequency domain plots that
illustrate the various categories used in the rule based method for
determining pulserate in accordance with one aspect of the present
invention.
FIG. 17 is a block diagram; which illustrates the signal processing
used to determine pulse rate by the pleth to pulserate transform
method (PPRT) in accordance with one aspect of the present
invention.
FIG. 18 is a flowchart showing the process steps of the rule based
pulserate detection method.
FIG. 19 illustrates a schematic diagram of a physiological monitor
that uses a neural network in accordance with the teachings of one
aspect of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
The present invention involves a system which uses first and second
measured signals that each contain a primary signal portion and a
secondary signal portion. In other words, given first and second
composite signals c.sub.1 (t)=s.sub.1 (t)+n.sub.1 (t) and c.sub.2
(t)=S.sub.2 (t)+n.sub.2 (t), the system of the present invention
can be used to isolate either the primary signal portion s(t) or
the secondary signal portion n(t) of the two composite signals.
Following processing, the output of the system provides a good
approximation n"(t) to the secondary signal portion n(t) or a good
approximation s"(t) to the primary signal portion s(t).
The system of the present invention is particularly useful where
the primary signal portion s(t), the secondary signal portion n(t),
or both, may contain one or more of a constant portion, a
predictable portion, an erratic portion, a random portion, etc. The
primary signal approximation s"(t) or the secondary signal
approximation n"(t) is derived by removing as many of the secondary
signal portions n(t) or primary signal portions s(t) from the
composite signal c(t) as possible. The remaining signal forms
either the primary signal approximation s"(t) or the secondary
signal approximation n"(t), respectively. The constant portion and
the predictable portion of the secondary signal n(t) are easily
removed with traditional filtering techniques, such as simple
subtraction, low pass, band pass, and high pass filtering. The
erratic portion is more difficult to remove due to its
unpredictable nature. If something is known about the erratic
signal, even statistically, it could be removed, at least
partially, from the measured signal via traditional filtering
techniques. However, often no information is known about the
erratic portion of the secondary signal n(t). In this case,
traditional filtering techniques are usually insufficient.
In order to remove the secondary signal n(t), a signal model in
accordance with the present invention is defined as follows for the
first and second measured signals c.sub.1 and c.sub.2 :
or
##EQU1##
(4)
where s.sub.1 and n.sub.1 are at least somewhat (preferably
substantially) uncorrelated and s.sub.2 and n.sub.2 are at least
somewhat (preferably substantially) uncorrelated. The first and
second measured signals c.sub.1 and c.sub.2 are related by
correlation coefficients r.sub.a and r.sub.v as defined above. The
use and selection of these coefficients is described in further
detail below.
In accordance with one aspect of the present invention, this signal
model is used in combination with a waveform scrubber to remove the
undesired portion of the measured signals.
The description that follows can best be understood in view of the
following list which briefly describes how the invention is broken
down and described according to the following topics:
1. A general overview of pulse oximetry measurements, in connection
with FIGS. 1 through 4, provides a general theory and system block
diagram for a red/infrared pulse oximetry apparatus for measurement
of physiological data such as blood oxygen saturation and
pulserate;
2. A more detailed description of the relationship between the data
RD(t) measured using red light, and the data IR(t) measured using
infrared light, normalization of RD(t) and IR(t), and the
relationship of the normalized RD(t) and IR(t) to blood oxygen
saturation, is provided in connection with FIGS. 5 through 8;
3. A mathematical model and description of the effect of motion
artifacts on RD(t) and IR(t) and a method for detecting and
removing thee artifacts to create a clean spectrum
F(.omega.)=RD(.omega.)/IR(.omega.), are provided in connection with
FIGS. 10 through 13;
4. A mathematical model and a description of a rule based signal
processing technique used by the pulse oximeter to determine
pulserate, are provided in connection with FIGS. 14 through 16;
and
5. A mathematical model and a description of a transform based
signal processing technique used by the pulse oximeter to determine
pulserate, are provided in connection with FIG. 17.
PULSE OXIMETRY MEASUREMENTS
A specific example of a physiological monitor using a processor of
the present invention to determine a secondary reference n'(t) for
input to a canceler that removes erratic motion-induced secondary
signal portions is a pulse oximeter. Pulse oximetry may also be
performed using a processor of the present invention to determine a
primary signal reference s'(t) which may be used for display
purposes or for input to a processor to derive information about
patient movement, pulserate, and venous blood oxygen
saturation.
A pulse oximeter typically causes energy to propagate through a
medium where blood flows close to the surface, for example, an ear
lobe, or a digit such as a finger, a forehead or a fetus' scalp. An
attenuated signal is measured after propagation through or
reflected from the medium. The pulse oximeter estimates the
saturation of oxygenated blood.
Freshly oxygenated blood is pumped at, high pressure from the heart
into the arteries for use by the body. The volume of blood in the
arteries and arterioles varies with the heartbeat, giving rise to a
variation in absorption of energy at the rate of the heartbeat, or
the pulse. The blood scatters both red and infrared light, and thus
as the volume of blood changes, the amount of scattering changes as
well. Typically the effects due to scattering are small when
compared to the effects due to the change in blood volume.
Oxygen depleted, or deoxygeniated, blood is returned to the heart
by the veins along with unused oxygenated blood. The volume of
blood in the veins varies with back pressure due to breathing as
well as local uncontrolled motion of muscles. These variations
typically occur at a rate that is much slower than the heartbeat.
Thus, when there is no motion induced variation in the thickness of
the veins, venous blood causes a low frequency variation in
absorption of energy. When there is motion induced variation in the
thickness of the veins, the scattering changes as well and this
absorption is coupled with the erratic variation in absorption due
to motion artifacts.
In absorption measurements using the transmission of energy through
a medium, two light emitting diodes (LEDs) are positioned close to
a portion of the body where blood flows close to the surface, such
as a finger, and a photodetector is positioned near the LEDs.
Typically, in pulse oximetry measurements, one LED emits a visible
wavelength, preferably red, and the other LED emits an infrared
wavelength. However, one skilled in the art will realize that other
wavelength combinations, as well as combinations of more than two
wavelengths, could be used. The finger comprises skin, tissue,
muscle, both arterial blood and venous blood, fat, etc., each of
which absorbs light energy differently due to different absorption
coefficients, different concentrations, different thicknesses, and
changing optical pathlengths. When the patient is not moving,
absorption is substantially constant except for the flow of blood.
The constant attenuation can be determined and subtracted from the
signal via traditional filtering techniques. When the patient
moves, this causes perturbation such as changing optical pathlength
due to movement of background fluids (e.g., venous blood having a
different saturation than the arterial blood). Therefore, the
measured signal becomes erratic. Erratic motion induced noise
typically cannot be predetermined and/or subtracted from the
measured signal via traditional filtering techniques. Thus,
determining the oxygen saturation of arterial blood and venous
blood becomes more difficult.
FIG. 3 depicts a general hardware block diagram of a pulse oximeter
299. A sensor 300 has two light emitters 301 and 302, such as
LED's. One LED 301 emitting light of red wavelengths, and another
LED 302 emitting light of infrared wavelengths are placed adjacent
a finger 310. A photodetector 320, which produces an electrical
signal corresponding to the attenuated visible and infrared light
energy signals is, located near the LED's 301 and 302. The
photodetector 320 is connected to front end analog signal
conditioning circuity 330.
The front end analog signal conditioning circuit 330 has outputs
coupled to an analog to digital conversion circuit 332. The analog
to digital conversion circuit 332 has outputs coupled to a digital
signal processing system 334. The digital signal processing system
334 provides the desired parameters as outputs for a display 336.
Outputs for display are, for example, blood oxygen saturation,
heart rate, and a clean plethysmographic waveform.
The signal processing system also provides an emitter current
control output 337 to a digital-to-analog converter circuit 338
which provides control information for a set of light emitter
drivers 340. The light emitter drivers 340 couple to the light
emitters 301, 302. The digital signal processing system 334 also
provides a gain control output 343 for the front end analog signal
conditioning circuitry 330.
FIG. 4 illustrates a preferred embodiment for the combination of
the emitter drivers 340 and the digital to analog conversion
circuit 338. As depicted in FIG. 4, the driver comprises first and
second input latches 321, 322, a synchronizing latch 323, a voltage
reference 324, a digital to analog conversion circuit 325, first
and second switch banks 326, 327, first and second voltage to
current converters 328, 329 and the LED emitters 301, 302
corresponding to the LED emitters 301, 302 of FIG. 3.
The preferred driver depicted in FIG. 4 is advantageous in that the
present inventors recognized that much of the noise in the oximeter
299 of FIG. 3 is caused by the LED emitters 301, 302. Therefore,
the emitter driver circuit of FIG. 4 is designed to minimize the
noise from the emitters 301, 302. The first and second input
latches 321, 322 are connected directly to the digital signal
processor (DSP) bus 337. Therefore, these action of these latches
significantly minimizes the bandwidth (resulting in noise) present
on the DSP bus 337 which passes through to the driver circuitry of
FIG. 4. The outputs of the first and second input latches 321, 322,
only change when the latches detect their respective address on the
DSP bus 337. The first input latch 321, receives the setting for
the digital to analog converter circuit 325. The second input latch
322 receives switching control data for the switch banks 326, 327.
The synchronizing latch 323 accepts the synchronizing pulses which
maintain synchronization between the activation of emitters 301,
302 and the analog to digital conversion circuit 332.
The voltage reference 324 is also chosen as a low noise DC voltage
reference for the digital to analog conversion circuit 325. In
addition, in the present embodiment, the voltage reference 324 has
a lowpass output filter with a very low corner frequency (e.g., 1
Hz in the present embodiment). The digital to analog converter 325
also has a lowpass filter at its output with a very low corner
frequency (e.g., 1 Hz). The digital to analog converter 338
provides signals for each of the emitters 301, 302.
In the present embodiment, the output of the voltage to current
converters 328, 329 are switched such that with the emitters 301,
302 connected in back-to-back configuration, only one emitter is
active an any given time. In addition, the voltage to current
converter 328 or 329 for the inactive emitter is switched off at
its input as well, such that it is completely deactivated. This
reduces noise from the switching and voltage to current conversion
circuitry. In the present embodiment, low noise voltage to current
converters are selected (e.g., Op 27 Op Amps), and the feedback
loop is configured to have a low pass filter to reduce noise. In
the present embodiment, the low pass filtering function of the
voltage to current converters 328, 329 has a corner frequency of
just above 316.7 Hz, which is the switching speed for the emitters,
as further discussed below. Accordingly, the preferred driver
circuit of FIG. 4, minimizes the noise of the emitters 301,
302.
In general, each of the red and infrared light emitters 301, 302
emits energy which is partially absorbed by the finger 310 and the
remaining energy is received by the photodetector 320. The
photodetector 320 produces an electrical signal which corresponds
to the intensity of the light energy striking the photodetector
320. The front end analog signal conditioning circuitry 330
receives the intensity signals and filters and conditions these
signals, as further described below, for further processing. The
resultant signals are provided to the analog-to-digital conversion
circuitry 332 which converts the analog signals to digital signals
for further processing by the digital signal processing system 334.
In the present embodiment, the output of the digital signal
processing system 334 provides clean plethysmographic waveforms of
the detected signals and provides values for oxygen saturation and
pulse rate to the display 336.
It should be understood that in different embodiments of the
present invention, one or more of the outputs may be provided. The
digital signal processing system 334 also provides control for
driving the light emitters 301, 302 with an emitter current control
signal on the emitter current control output 337. This value is a
digital value which is converted by the digital-to-analog
conversion circuit 338 which provides a control signal to the
emitter current drivers 340. The emitter current drivers 340
provide the appropriate current drives for the red emitter 301 and
the infrared emitter 302. Further detail of the operation of the
physiological monitor for pulse oximetry is explained below.
In the present embodiment, the light emitters 301, 302 are driven
via the emitter current driver 340 to provide light transmission
with digital modulation at 316.7 Hz. In the present embodiment, the
light emitters 301, 302 are driven at a power level which provides
an acceptable, intensity for detection by the detector and for
conditioning by the front end analog signal conditioning circuitry
330. Once this energy level is determined for a given patient by
the digital signal processing system 334, the current level for the
red and infrared emitters is maintained constant. It should be
understood, however, that the current may be adjusted for changes
in the ambient room light and other changes which would effect the
voltage input to the front end analog signal conditioning circuitry
330. In the present invention, the red and infrared light emitters
301, 302 are modulated as follows: for one complete 316.7 Hz red
cycle, the red emitter 301 is activated for the first quarter
cycle, and off for the remaining three-quarters cycle; for one
complete 316.7 Hz infrared cycle, the infrared light emitter 302 is
activated for one quarter cycle and is off for the remaining
three-quarters cycle. In order to only receive one signal at a
time, the emitters are cycled on and off alternatively, in
sequence, with each only active for a quarter cycle per 316.7 Hz
cycle and with a quarter cycle separating the active times.
The light signal is attenuated (amplitude modulated) by the pumping
of blood through the finger 310 (or other sample medium). The
attenuated (amplitude modulated) signal is detected by the
photodetector 320 at the 316.7 Hz carrier frequency for the red and
infrared light. Because only a single photodetector is used, the
photodetector 320 receives both the red and infrared signals to
form a time division multiplexed (TDM) signal. The TDM signal is
provided to the front analog signal conditioning circuitry 330 and
may be demodulated by either before or after analog to digital
conversion.
Saturation Curves and Normalization
The ability of the apparatus 299 to measure the desired physiologic
properties lies in the optical absorption properties of hemoglobin,
as illustrated in FIG. 5. FIG. 5 shows an x axis 501 corresponding
to a wavelength of light and a y axis 502 corresponding to an
absorption coefficient for light passing through a medium. A
reduced hemoglobin (Hb) curve 503 shows the absorption properties
of oxygen poor hemoglobin. An oxygen rich hemoglobin (HbO2) curve
504 shows the absorption properties of oxygen rich hemoglobin. A
reference line 506 highlights the region where the curves 503 and
504 pass through a value on the x axis 501 corresponding to 660 nm
(nanometer) wavelength (the nominal operational-wavelength of the
red emitter 301). A reference line 505 highlights the region where
the curves 503 and 504 pass through a value on the x axis 501
corresponding to 905 nm wavelength (the nominal operational
wavelength of the infrared emitter 302).
At the reference line 506, the Hb curve 503 shows more absorption
than the HbO2 curve 504. Conversely, at the reference line 505, the
HbO2 curve shows more absorption than the Hb curve 503. The pulse
oximeter can thus measure the blood oxygen saturation by measuring
absorption of the blood at 660 nm and 905 nm, and the comparing the
two absorption measurements.
According to the Beer-Lambert law of absorption, the intensity of
light transmitted through an absorbing medium is given by:
where I.sub.0 is the intensity of the incident light, .epsilon. is
the absorption coefficient, c is the concentration coefficient and
d is the thickness of the absorbing medium. In pulse oximetry
applications, there are two sources, red and infrared, and thus two
incident intensities, I.sub.0,RD for red, and I.sub.0,IR for
infrared. Furthermore, in blood there are two concentrations of
interest, namely the concentration of oxygen poor hemoglobin,
denoted by C.sub.Hb and the concentration of oxygen rich
hemoglobin, denoted by C.sub.HbO2. The combination of the two
optical wavelengths and the two concentrations means that there are
four absorption coefficients, namely .epsilon..sub.RD,Hb,
.epsilon..sub.RD,HbO2, .epsilon..sub.IR,Hb, and
.epsilon..sub.IR,HbO2. Using these quantities, and assuming no time
variation in any of the values except d, gives two separate
Beer-Lambert equations for the pulse oximeter.
The measurement apparatus 299 does not provide a capability for
measuring the incident terms I.sub.0,RD and I.sub.0,IR appearing in
the above equation, and thus, strictly speaking, the value of
I.sub.RD and I.sub.IR cannot be determined. However, in the pulse
oximeter, only differential measurements are necessary. In other
words, it is only the time varying nature of the values I.sub.RD
and I.sub.IR and the relationship between the values that are
important. The time variation in d(t) occurs primarily because
blood flows in and out of the finger with each heartbeat. As blood
flows into the finger, the effective value of d, as well as the
scattering component, increases, and as blood flows out, the
effective value of d and the scattering decreases. There are also
time variations in the concentrations C.sub.Hb and C.sub.HbO2 as
the blood oxygen saturation level changes. Fortunately, these
variations are slow compared to the variations in d(t), and they
can be ignored.
FIG. 6 illustrates one cycle of an idealized plethysmographic
waveform for various levels of oxygen saturation. The figure shows
an x-y plot having a time axis 601 in the x direction, and a
transmission axis 602 in the y direction. The transmission axis 602
shows the intensity of the red light transmitted through the
finger. A curve 604 shows the transmission of red light for 80%
blood oxygen saturation. A curve 603 shows transmission of red
light for 98% blood oxygen saturation. The curves 603 and 604 are
intended to show different values of saturation given the same
perfusion d. As shown in the figure, at the beginning of a
heartbeat, red transmission is at a maximum because the finger
contains relatively little blood. As the heartbeat progresses,
blood is perfused into the finger and the amount of light
transmission diminishes. Transmission diminishes because the
additional material, the blood, increases the effective path length
d in Equation (6). Transmission also diminishes somewhat because of
scattering produced by the blood. If the blood is highly saturated
with oxygen, as shown in the curve 603, the transmission diminishes
only slightly because, as shown in FIG. 5, HbO2 has a relatively
small absorption coefficient in the red wavelengths. If the blood
has low oxygen saturation, as shown in the curve 604, then
transmission diminishes significantly more because, as shown in
FIG. 5, Hb has a relatively large absorption in the red
wavelengths.
If FIG. 6 were redrawn to show the transmission properties of
infrared light, then the curves 603 and 604 would essentially be
interchanged, because as shown in FIG. 5, more infrared light is
absorbed by HbO2 than is absorbed by Hb.
The above properties of the absorption of light by Hb and HbO2
advantageously provide a way to measure blood oxygen saturation by
computing the ratio of red light to infrared light. FIG. 7 shows
one embodiment of a signal processing apparatus for obtaining the
desired ratio. FIG. 7 shows a red signal path which begins at a RD
signal input 701. The RD signal input 701 corresponds to the amount
of red light transmitted through the finger. The signal at the RD
signal input 701 is fed into a logarithmic amplifier 702 which in
turn feeds a bandpass filter 703. The output of the bandpass filter
703 is fed into a root-means-square (RMS) detector 704. The output
of the RMS detector 704 is fed to a numerator input of a divider
709. FIG. 7 further shows an IR signal path comprising an IR input
705, a logarithmic amplifier 706, a bandpass filter 707, and an RMS
detector 708. The output of the RMS detector 708 is fed to a
denominator input of the divider 709.
In a preferred embodiment, the elements shown in FIG. 7 are part of
the signal processing block 334 shown in FIG. 3. The RD input 701
and IR input 705 are obtained by demultiplexing the output of the
detector 320, also shown in FIG. 3. The signals at the inputs 701
and 705 correspond to I.sub.RD and I.sub.IR respectively, and are
similar to the curves shown in FIG. 6. However, in the preferred
embodiment, the signals are uncalibrated (i.e., the scale of the y
axis 602 is unknown) because the value of I.sub.0,RD and I.sub.0,IR
in Equations (6) and (7) are unknown. This is not an impediment to
the measurement of the blood oxygen saturation, because saturation
can be obtained without reference to either I.sub.0,RD or
I.sub.0,IR as follows. Taking the natural logarithm (in signal
processing blocks 702 and 706) of both Equation (6) and Equation
(7) yields:
ln(I.sub.RD)=ln(I.sub.0,RD)-[.epsilon..sub.RD,Hb C.sub.Hb
+.epsilon..sub.RD,HbO2 C.sub.HbO2 ]d(t) (8)
Applying a bandpass filter (in signal processing blocks 703 and
707) removes the non-time varying components, and allows Equations
(8) and (9) to be rewritten as:
FIG. 9 shows a plot of RD(t) versus IR(t). In FIG. 9, an x axis 810
corresponds to IR(t) and a y axis 811 corresponds to RD(t). A
straight line 812, having a positive slope, illustrates how the
plot of RD(t) versus IR(t) would appear under ideal conditions of
no noise, no scattering, and no motion artifacts. A curve 813
depicts a more realistic locus of points RD(t) versus IR(t) under
normal measurement conditions. FIG. 8 shows a plot of blood oxygen
saturation versus the ratio of the RMS value of RD(t)/IR(t). FIG. 8
shows an x axis 801 corresponding to blood oxygen saturation from
0% to 100% and a y axis corresponding to RMS(RD(t))/RMS(IR(t))
ranging from 0 to 3. A saturation curve 803 depicts the
relationship between RMS(RD(t))/RMS(IR(t)) and blood oxygen
saturation. The blood oxygen saturation is given by
sat=100*C.sub.HbO2 /(C.sub.Hb +C.sub.HbO2) It is obtained by
dividing Equation (10) by Equation (11) and solving for C.sub.HbO2
and C.sub.Hb using the measured values of RD(t) and IR(t), and the
known values of the absorption coefficients. Note that the unknown
quantity d(t) is approximately the same for both red and infrared
and thus divides out.
Detection and Removal of Motion Artifacts
Persons skilled in the art know that the data obtained during,
pulse oximetry measurements using red and infrared light are often
contaminated due to motion. Identification and removal of these
motion artifacts is often a prerequisite to any signal processing
used to obtain blood oxygen saturation, pulserate, or other
physiological data. FIG. 10 schematically illustrates an additive
noise process model that can be used, in conjunction with Equation
(10) and Equation (11) to approximate the measured data
contaminated by such motion artifacts. FIG. 9 shows a desired
signal input s(t) 901 and an undesired signal input n(t) 902. The
desired signal s(t) 901 and the undesired signal input n(t) 902 are
summed by a summing junction 903. The output of the summing
junction 903 represents the actual measured data M(t) 904. As
applied to Equation (10), the desired signal s(t) 901 corresponds
to RD(t). As applied to Equation (11), the desired signal s(t) 901
represents IR(t).
In FIG. 10, the desired signal s(t) 901, which contains the desired
physiologic data is not directly accessible. Only the measured
signal M(t) 904 is accessible. Thus, the problem is to obtain an
estimate of the undesired signal n(t) 902 so that it can be
subtracted from the measured signal to yield the desired signal.
One such method for removing the undesired signal n(t) involves the
use of a correlation canceler as is found in U.S. Pat. No.
5,432,036 (the '036 patent) assigned to the same assignee as the
present application.
The correlation canceler is a complex operation requiring
significant computational overhead. In accordance with one
embodiment of the present invention, a new and novel method for
detecting the presence of motion artifacts and removing these
artifacts can be found in the spectral domain representations of
the signals RD(t) and IR(t). Use of the spectral domain
representations is more compatible with many of the digital signal
processor (DSP) devices currently available. Further, the use of
the spectral domain representations provides a method, as disclosed
below, a way to estimate the amount of motion and noise separately.
As a further advantage, it is noted that, under certain
circumstances, the correlation canceler would drive the output
signal to zero. The spectral domain method of detecting artifacts
is far less likely to drive the output signal to zero.
FIG. 11 shows an idealized illustration of the spectrum of RD(t)
and IR(t). FIG. 11 shows an x axis 1101 corresponding to frequency,
and a y axis 1102 corresponding to the magnitude of the spectral
components. The spectrum of RD(t), denoted mathematically as:
is shown as a series of peaks, comprising a first spectral peak
1104 at a fundamental frequency f.sub.0, a second spectral peak
1107 at a first harmonic f.sub.1 and a third spectral peak 1110 at
a frequency f.sub.m. The spectrum of IR(t), denoted mathematically
as:
is shown as a series of peaks, comprising a first spectral peak
1103 at the fundamental frequency f.sub.0, a second spectral peak
1106 at the first harmonic f.sub.1 and a third spectral peak 1109
at a frequency f.sub.m. The ratio of the spectral components, given
by RD(.omega.)/IR(.omega.), is shown as a first ratio line 1105 at
the fundamental frequency f.sub.0, a second ratio line 1108 at the
first harmonic f.sub.1 and a third ratio line 1111 at the frequency
f.sub.m. As discussed below, when there are no motion artifacts in
the spectrum of FIG. 11, all of the spectral peaks will occur at
harmonic frequencies, and all of the ratio lines will have
approximately the same height. Under conditions of no motion,
difference in the height of the ratio lines will be due primarily
to scattering effects. The spectral peaks 1110 and 1109
corresponding to the frequency f.sub.m, which is not necessarily a
harmonic of f.sub.0, represent peaks due to motion, and therefor
having an amplitude different from that of the first spectral line
1105 and the second spectral line 1108.
One skilled in the art will recognize that the representations in
FIG. 11 have been idealized for the purposes of explanation. In
particular, in actual measured data, especially data contaminated
by noise and other undesired components, the frequencies of the
spectral peaks of RD(.omega.) do not correspond exactly to the
spectral peaks of IR(.omega.). Although corresponding frequencies
will typically be quite close, variations of a few percent are not
unexpected. Thus, for example, it will be obvious to one skilled in
the art that, due to the imperfections in most measured data, the
fundamental frequency f.sub.0 found for RD(.omega.) will often be
different from the fundamental frequency f.sub.0 found for
IR(.omega.). The same comments would apply to other harmonics
(e.g., f.sub.1 and f.sub.2) as well. In one embodiment of the
present invention, the frequencies f.sub.0, f.sub.1, f.sub.2 (or
equivalently .omega..sub.0, .omega..sub.1, .omega..sub.2), etc.
(hereinafter the frequency peaks) correspond to the frequency peaks
found in RD(.omega.), and the ratios RD(.omega.)/IR(.omega.) are
calculated using the values of RD(.omega.) and IR(.omega.) at those
frequencies, regardless of whether they also happen to correspond
to a frequency peak in IR(.omega.). In another embodiment of the
present invention, the frequency peaks correspond the frequency
peaks found in IR(.omega.), and the ratios RD(.omega.)/IR(.omega.)
are calculated using the values of RD(.omega.) and IR(.omega.) at
those frequencies, regardless of whether they also happen to
correspond to a frequency peak in RD(.omega.). In yet another
embodiment of the present invention, the frequency peaks of
RD(.omega.) and IR(.omega.) are found separately, and the ratios
RD(.omega.)/IR(.omega.) are calculated by matching the frequency
peaks of RD(.omega.) with the nearest frequency peaks of
IR(.omega.).
In an ideal measurement, the red and infrared spectra are the same
to within a constant scale factor. Thus, in an ideal measurement,
all of the ratio lines 1105, 1108 and 1111 have substantially the
same amplitude. Any differences in the amplitude of these lines is
likely due to motion or other contaminations represented by n(t)
(including scattering effects). For each component, red and
infrared, the model of FIG. 9 can be expressed as: ##EQU2##
where S.sub.1 (t) represents the infrared signal, A(t) represents
the desired infrared signal and N(t) represents the noise signal.
Likewise, S.sub.2 (t) represents the measured red signal, r
represents the ratio of red to infrared (RD(.omega.)/IR(.omega.))
expected in an uncontaminated measurement, and .mu. represents the
ratio of red noise to infrared noise. The quantities h(t) and
.eta.(t) are primarily due to scattering, and thus required
because, strictly speaking, A(t) and N(t) in the red channel and
infrared channels are not simply related by a constant. However,
for most purposes, the quantities h(t) and .eta.(t) are
sufficiently close to unity that they can be ignored.
Introducing an arbitrary scaling factor a into the equation for
S.sub.1, and then subtracting the two equations yield (for
notational convenience, the time dependence of S, A and N will not
be explicitly shown):
Two special cases arise from Equation (17). First, when cry,
Equation (17) reduces to: ##EQU3##
Second, when .alpha.=.mu., Equation (17) reduces to: ##EQU4##
The values of .mu. and r can be found from the ratio of
RD(.omega.)/IR(.omega.) as shown in FIG. 11 and the following two
observations. First, since r is the coupling coefficient between
red and infrared (the ratio of red to infrared) then r is expected
to be reasonably constant over short periods of time. Likewise,
.mu. is expected to be relatively constant because it is merely the
coupling coefficient between the noise in the red and infrared
signals. Second, the condition .mu.=r is not expected to occur
because that would mean that the saturation due to arterial blood
is equal in magnitude to the saturation due to venous blood. One
skilled in the art will recognize, that, except for short periods
of time, arterial blood saturation and venous blood saturation
cannot be the same, because a living body consumes oxygen from the
blood as the blood passes from the arteries to the veins. Arterial
blood and venous blood saturation can be the same for short periods
of time, and even reversed, especially where blood pooling has
occurred and a quick desaturation is taking place. It is always
expected that .mu. is larger than r. Therefore, in one embodiment
of the present invention, the value of .mu. corresponds to the
largest peak in FIG. 11 and the value of r corresponds to the
smallest peak of FIG. 11. Further, the presence of motion artifacts
in the data are easily detectable by examination of the
relationship between .mu. and r.
In a preferred embodiment, the value of .mu. is found by
classifying the ratio peaks according to a ratio threshold g. The
ratio threshold g is computed identifying the first N ratio lines
R.sub.N associated with the first N spectral peaks. The ratio
threshold g is then computed as a modified center of mass for the
R.sub.N lines according to the following equation. ##EQU5##
Each ratio line is then compared with the ratio threshold g. Only
those ratio lines whose magnitude is larger than the ratio
threshold g are included in a set Y of ratio lines. Only ratio
lines in the set Y are used in the calculation of .mu.. In one
embodiment, the value of .mu. is the magnitude of the largest ratio
peak in the set of ratio peaks R.sub.i for i=0 . . . N. In an
alternate embodiment, the value of .mu. is the magnitude of the
ratio peak corresponding to the largest spectral peak in the set
Y.
The values of .mu. and r are used to determine whether motion
artifacts are present. In one embodiment, the ratio .mu./r is
calculated. If the ratio is close to unity, then, to within a
constant scaling factor, the spectrum RD(.omega.) is approximately
the same as the spectrum IR(.omega.) and thus there are no motion
artifacts. If, on the other hand, the ratio .mu./r is not close to
unity, then the shape of the spectrum RD(.omega.) is different from
the spectrum IR(.omega.), signaling the presence of motion
artifacts, and thus the spectrum must be scrubbed according to
Equation (17).
In a preferred embodiment, a delta is computed by subtracting the
magnitude of the smallest ratio line from the magnitude of the
largest ratio line. If the delta is smaller than a threshold value,
then the spectrum RD(.omega.) is approximately the same as the
spectrum IR(.omega.) and thus there are no motion artifacts, but
only variations due to scattering. If, on the other hand, the delta
.mu.-r is greater than the threshold value, then the shape of the
spectrum ID(.omega.) is different from the spectrum IR(.omega.),
signaling the presence of motion artifacts, and thus the spectrum
must be scrubbed according to Equation (19).
FIG. 12 shows a block diagram of a signal processing system that
implements the motion detection and spectrum scrubbing operations
in accordance with one aspect of the present invention. In FIG. 12,
an input from a single sensor 1202 that receives red and infrared
light is fed into a demultiplexer 1204 which separates the red and
infrared signals. The red signal is fed into a filter 1206 which
removes unwanted spectral components. The output of the filter 1206
is normalized (as is described in the text describing FIG. 7) by
the series combination of a log amplifier 1208, and a bandpass
filter 1210. The output RD(t) of the bandpass filter 1210 is fed
into a Fourier transform block 1214. The output of the transform
block 1214 is fed into the numerator term of a divider 1230. The
infrared output from the demultiplexer 1204 is processed, in the
same fashion as the red signal, by the series combination of a
filter 1220, a log amplifier 1222, a bandpass filter 1224, and a
Fourier transform block 1228. The output of the Fourier transform
block 1228 is fed into a denominator input of the divider 1230. An
output of the divider 1230 is fed into a process block 1240 which
determines .mu., and r, and which computes a according to the
flowchart of FIG. 13. An a output of the process block 1240 is fed
as an input to a time domain waveform scrubber 1242. The time
domain waveform scrubber 1242 has three input terminals, A, B, and
D, and a single output terminal C. The time domain scrubber
terminal A is connected to the output of the bandpass filter 1210.
The time domain scrubber terminal B is connected to the output of
the bandpass filter 1224. The time domain scrubber terminal D is
connected to the a output of the process block 1240. Inside the
time domain scrubber 1242, the terminal A is connected to a signal
input of a gain controlled amplifier 1244. A gain control input of
the amplifier 1244 is connected to the scrubber terminal D. The
scrubber terminal B is connected to a plus input of an adder 1246.
An output of the amplifier 1244 is connected to a minus input of
the adder 1246. An output of the adder 1246 is connected to a
Fourier transform block 1248. An output of the Fourier transform
block 1248 is connected to the scrubber output terminal C.
One skilled in the art will recognize that the linearity of the
Fourier transform allows the scrubbing operation to be carried out
in the frequency domain as well. A frequency domain scrubber 1240
is also shown in FIG. 12. The frequency domain scrubber 1260 has
the same four terminals, A, B, C, and D, as the time domain
scrubber 1242.
Inside the frequency domain scrubber 1260, the terminal A is
connected to a signal input of a Fourier transform block 1262. The
output of the Fourier transform block 1262 is connected to a signal
input of a gain controlled amplifier 1266. A gain control input of
the amplifier 1266 is connected to the scrubber terminal D. The
scrubber terminal B is connected to a Fourier transform block 1264.
An output of the transform block 1264 is connected to a plus input
of an adder 1268. An output of the amplifier 1266 is connected to a
minus input of the adder 1268. An output of the adder 1268 is
connected to the scrubber output terminal C.
Regardless of whether the time domain scrubber 1242 or the
frequency domain scrubber 1260 is used, the scrubber output C is a
plethysmographic waveform in the frequency domain at a terminal
1249. Ideally, the waveform at terminal 1249 is cleaner (e.g., has
a better signal to noise ratio) than the waveform at either
scrubber input A or scrubber input B. The waveform at terminal 1249
can be displayed on a display (not shown) or sent to a rule based
pulserate detector 1250 and/or a transform based pulserate detector
1252.
FIG. 13 is a flowchart which illustrates the process steps
performed by the signal processing block 1240 in FIG. 12. The
flowchart of FIG. 13 begins at a start block 1302 and proceeds to a
process block 1304. In the process block 1304, the spectrum
F(.omega.)=RD(.omega.)/IR(.omega.) is searched for the largest
ratio line .mu. and smallest ratio line r and the frequencies
f.sub..mu. and f.sub.r at which those two lines occur. The process
then advances to a process block 1306 where the difference, delta
d=.mu.-r is computed. The process then proceeds to a decision block
1308. If, in the decision block 1308, the delta d is greater than a
threshold value, then motion artifacts are present and the process
advances to a decision block 1312 to continue the calculation of
.alpha.. Otherwise, if in the process block 1308, the delta d is
less than the threshold value, then no scrubbing is necessary and
the process advances to a process block 1310. Since both .mu. and r
are ratios, they are dimensionless. The delta d is also
dimensionless. In a preferred embodiment, the threshold value is
0.5. In the process block 1310, the value of .alpha. is set to 0,
which essentially disables the scrubber. In the decision block
1312, the frequencies f.sub..mu. and f.sub.r are compared. If the
two frequencies are close together, then the process advances to a
process block 1314; otherwise, the process advances to a process
block 1316. In the process block 1314 the value of .alpha. is set
to .alpha.=(.mu.+r)/2. In the process block 1316 the value of
.alpha. is set to a=.mu.. The process blocks 1310, 1314 and 1316
all advance to a process block 1318 where the value of .alpha. is
sent to the scrubber. Upon completion of the process block 1318,
the process jumps back to the process block 1304 to recalculate
.alpha..
One skilled in the art will recognize that the flowchart in FIG. 13
can be modified to perform additional functions. For example, upon
detecting that motion artifacts are present (during the transition
to the decision block 1312), an indicator can be lit, or an alarm
can be sent, to warn the medical clinician that motion artifacts
were present. In yet another embodiment, upon transitioning to the
process block 1312, the delta d could be examined against a second
threshold to determine whether the motion artifacts were so severe
that furthers processing was impossible.
Rule Based Pulserate Detection
In addition to measuring blood oxygen saturation, a pulse oximeter
is able to perform continuous monitoring of a patient's pulserate.
As shown in FIG. 6, each heartbeat forces blood into the arteries
and that increase in blood is detected by the plethysmographic
apparatus. Thus, the scrubbed spectrum present at the terminal 1250
in FIG. 12 contains some of the information that would be found in
the Fourier spectrum of an electrocardiograph (EKG).
FIG. 14A shows an ideal spectrum F(.omega.) of a clean
plethysmographic wave from a heart that is beating with a very
regular beat. The figure shows an x axis 1410 corresponding to
frequency and a y axis 1411 corresponding to the magnitude of the
spectral components. A curve 1412 shows
.vertline.F(.omega.).vertline.. It is well known, that the waveform
of a human heartbeat is not a pure sine wave, and thus the curve
1412 is not a single spectral line, but rather a first spectral
line at a fundamental frequency f.sub.0 and a series of decreasing
harmonics at 2f.sub.0, 3f.sub.0, etc. Clearly, under these
conditions, the frequency f.sub.0 corresponds to the pulserate.
Often the ideal waveform of FIG. 14A is not seen because the heart
is beating irregularly or because the cardiovascular system of the
subject is producing a large dicrotic notch. This leads to a
spectrum in which the largest spectral line is not necessarily the
pulserate. FIG. 14B shows one example of such a waveform. Like FIG.
14A, FIG. 14B shows an x axis 1420 corresponding to frequency, and
a y axis 1421 corresponding to amplitude. A curve 1422 shows
.vertline.F(.omega.).vertline.. However, unlike the curve 1412, the
curve 1422 shows a spectral line at a fundamental frequency
f.sub.0, and a series of harmonics f.sub.1 and f.sub.2 having
amplitudes larger than the amplitude of the fundamental, with
f.sub.2 being the largest. The curve 1422 illustrates the folly of
attempting to determining pulserate merely by finding the largest
spectral line. Such an algorithm, applied to the curve 1422 would
report a pulserate that was three times higher than the actual
pulse rate.
The spectrum shown in curve 1422 is commonly seen in
plethysmographic waveforms and corresponds to a frequency modulated
(FM) heartbeat. In accordance with one aspect of the present
invention, a rule based method for determining the pulserate of a
heart producing the spectrum of FIG. 14B is disclosed. The rule
based method is based on a time domain model (a "stick model")
plotted in FIG. 14C. This elegantly simple model captures the
essential feature of the plethysmographic waveform. FIG. 14C shows
an x axis 1401 corresponding to time, and a y axis 1402
corresponding to the amount of blood being forced into the arteries
by a heart. FIG. 14C further shows two overlapping waveforms. A
first waveform 1403 shows to blood being forced into the arteries
during a first time interval To. A second waveform 1404 shows blood
being forced into the arteries during a second time interval
T.sub.2 The two time intervals, T.sub.1 and T.sub.2, do not overlap
and the total period of the sum of the two waveforms is T.sub.1
+T.sub.2. The sum of the two waveforms represents a heart that is
beating at two different pulserates on alternate beats. For
example, if the heartbeats were numbered, then on every even
numbered beat, the heartbeat would last T.sub.1 seconds. On every
odd numbered beat, the heartbeat would last T.sub.2 seconds. This
is not an unusual occurrence, and there are physiological reasons
why this occurs. The spectrum shown in FIG. 14B is essentially the
spectrum of the superposition of the waveforms 1403 and 1404.
Amplitude modulation (AM) of the plethysmographic waveform is also
possible and common. Amplitude modulation occurs primarily when the
heart beats with different strength on different heartbeats. FIG.
15 shows a sample spectrum F(.omega.) that exhibits the effects of
AM. FIG. 15 shows a frequency axis 1501 and a spectrum axis 1502.
The spectrum consists of a series of spectral peaks 1503 and
sidebands 1504. One skilled in the art will recognize this as a
typical AM spectrum of a carrier and its associated modulation
sidebands. Under some conditions, of high pulserate and substantial
modulation bandwidth, the sidebands 1504 due to one spectral peak
1503 can overlap the sidebands due to an adjacent spectral peak.
This overlap significantly complicates the waveform, (not
shown).
In accordance with one aspect of the present invention, the
pulserate can be determined in the presence of FM and AM
distortions by classifying the spectrum as one of five categories
grouped into three cases. The five categories are illustrated as
idealized graphs in: FIG. 16A, illustrating Case I; FIG. 16B,
illustrating Case II;
and FIG. 16C, illustrating Case III.
FIG. 16A shows a plot 1600 having an x axis 1601 corresponding to
frequency and a y axis 1602 corresponding to the magnitude of the
spectrum. FIG. 16A also shows a first spectral line 1603, a second
spectral line 1604 and a third spectral line 1605. The three
spectral lines 1603, 1604, and 1605 show a monotonically decreasing
amplitude where the decrease is approximately linear.
FIG. 16B shows a first plot 1610 having an x axis 1611
corresponding to frequency and a y axis 1612 corresponding to the
magnitude of the spectrum. The first plot 1610 also shows a first
spectral line 1613, a second spectral line 1614 and a third
spectral line 1615. The third spectral line 1615 has the smallest
amplitude of the three lines. The second spectral line 1614 has the
largest amplitude of the three lines, and its amplitude rises
significantly above a line drawn from the first spectral line 1613
to the third spectral line 1615.
FIG. 16B also shows a second plot, 1620 having an x axis 1621
corresponding to frequency and a y axis 1622 corresponding to the
magnitude of the spectrum. The second plot 1620 also shows a first
spectral line 1623, a second spectral line 1624 and a third
spectral line 1625. The first spectral line 1623 has the smallest
amplitude of the three lines. The second spectral line 1624 has the
largest amplitude of the three lines, and its amplitude rises
significantly above a line drawn from the first spectral line 1623
to the third spectral line 1625.
FIG. 16C shows a first plot 1630 having an x axis 1631
corresponding to frequency and a y axis 1632 corresponding to the
magnitude of the spectrum. The first plot 1630 also shows a first
spectral line 1633, a second spectral line 1634 and a third
spectral line 1635. The amplitudes of the three spectral lines are
monotonically increasing, and the increase is approximately
linear.
FIG. 16C also shows a second plot 1640 having an x axis 1641
corresponding to frequency and a y axis 1642 corresponding to the
magnitude of the spectrum. The second plot 1640 also shows a first
spectral line 1643, a second spectral line 1644 and a third
spectral line 1645. The third spectral line 1645 has the smallest
amplitude of the three lines. The second spectral line 1644 has the
largest amplitude of the three lines, and its amplitude is
significantly below a line drawn from the first spectral line 1643
to the second spectral line 1645.
In accordance with one aspect of the present invention, the
pulserate is determined by identifying the largest three spectral
lines, then matching the spectrum to one of the idealized spectra
shown by the plots 1600, 1610, 1620, 1630, or 1640, and then
applying one of a set of rules to determine the pulserate. It will
be understood by one skilled in the art that, although the
frequencies of the spectral shown in the plots 1600, 1610, 1620,
1630, or 1640 appear to be harmonically related. In practice the
spectral lines may not correspond to frequencies which are
harmonics.
The details of the rule based process are shown in the flowchart of
FIG. 18. FIG. 18 begins at a start block 1802 and proceeds to an
initialization process block 1804. In the block 1804, the values of
the pulserate, p, and confidence factor, s, are set to zero. When
the process reaches an exit block, p will contain the pulserate (or
zero if no pulserate was found), and .sigma. will contain a
confidence factor indicating related to the pulserate (or zero if
no pulserate was found). After completing the initialization block
1804, the process advances to a search process block 1805 where the
spectrum .vertline.F(.omega.).vertline. is searched for the first
three spectral peaks. After finding the peaks, the process advances
to a decision block 1806 where the process checks the number of
spectral peaks actually found. If, in the decision block 1806, the
number of peaks is less than three, then the process advances to a
decision block 1808; otherwise, the process jumps forward to a
process block 1812. If, in the process block 1808, the number of
peaks is greater than zero, then the process advances to a process
block 1810; otherwise, the process jumps to an exit block. In the
process block 1810, the value of p is set to the frequency
corresponding to the largest of the spectral peaks, the confidence
value is set to 10, and the process then advances to the exit
block.
In the process block 1812, the first three spectral peaks are
sorted by magnitude, and the values assigned to variables A.sub.0,
A.sub.1, and A.sub.2 such that A.sub.0 is the magnitude of the
largest peak, A.sub.1 is the magnitude of the middle peak, and
A.sub.2 is the magnitude of the smallest peak. Also, in the process
block 1812, variables f.sub.0, f.sub.1, and f.sub.2, representing
the frequencies corresponding to A.sub.0, A.sub.1 and A.sub.2
respectively, are set. Upon completion of the process block 1812,
the process advances to a decision block 1814. In the decision
block 1814, if A.sub.0 is greater than or equal to 1.2*(A.sub.1
+A.sub.2) and f.sub.0 is less than 250, then the process advances
to a process block 1816; otherwise the process jumps to a decision
block 1824. In the process block 1816, the value of p is set to
p=f.sub.0, and the process then advances to a decision block 1818.
In the decision block 1818, the values of f.sub.0, f.sub.1, and
f.sub.2 are checked to see if they are harmonics of one another. In
a preferred embodiment, this is done by checking to see whether a
frequency f.sub.i is within ten beats per minute of being a integer
multiple of another frequency f.sub.j (where i,j=0, 1, or 2). If
the decision block 1818 detects that the frequencies are harmonics,
then the process advances to a process block 1820; otherwise, the
process advances to a process block 1822. In the process block
1820, the value of .sigma. is set to 60, and the process then
advances to the decision block 1824. In the process block 1822, the
value of .sigma. is set to 50 and the process then advances to the
decision block 1824. In the decision block 1824, if A.sub.0
<1.2*(A.sub.1 +A.sub.2); then the process advances to a decision
block 1826, otherwise the process advances to the exit block. In
the decision block 1826, if (f.sub.0 <f.sub.1) and (f.sub.0
<f.sub.2), then the process advances to a decision block 1828;
otherwise the process advances to a decision block 1938. In the
decision block 1828, if the frequencies f.sub.0, f.sub.1, and
f.sub.2 are harmonics, then the process advances to a decision
block; otherwise, the process advances to a process block 1836. In
the process block 1836, the value of p is set to p=f.sub.0, the
value of .sigma. is set to 90, and the process then advances to the
decision block 1838. In the decision block 1830, if f.sub.0 is less
than 45 beats per minute, then the process advances to a process
block 1834; otherwise, the process advances to a process block
1832. In the process block 1832, the value of p is set to
p=f.sub.0, the value of .sigma. is set to .sigma.=80, and the
process then advances to the decision block 1838. In the process
block 1834, the value of p is set to p=(f.sub.0 +f.sub.1
+f.sub.2)/3, the value of .sigma. is set to .sigma.=70, and the
process then advances to the decision block 1838.
In the decision block 1838, if f.sub.0 >f.sub.1 or f.sub.0
>f.sub.2, then the process advances to a decision block 1840;
otherwise, the process advances to a decision block 1846. In the
decision block 1840, if (f.sub.0 >f.sub.1) and (f.sub.0, f.sub.1
and f.sub.2 are harmonics) and (A.sub.0 <1.7 A.sub.1) and
(30<f.sub.1 <130) then the process advances to a decision
block 1842; otherwise, the process advances to a process block
1844. In the process block 1842, the value of p is set to
p=f.sub.1, the value of .sigma. is set to .sigma.=100, and the
process then advances to the decision block 1848. In the process
block 1844, the value of p is set to p=f.sub.0, the value of
.sigma. is set to .sigma.=110, and the process then advances to the
decision block 1848.
In the decision block 1848, if (f.sub.0, f.sub.1 and f.sub.2 are
harmonics) and f.sub.0 <100 and ((A.sub.1 +A.sub.2)/A.sub.0
>1.5), then the process advances to a process block 1852;
otherwise, the process advances to a process block 1850. In the
process block 1852, the value of p is set to p=(f.sub.0 +f.sub.1
+f.sub.2)/3, the value of .sigma. is set to .sigma.=120, and the
process then advances to the exit block. In the process block 1852,
the value of p is set to p=f.sub.0, the value of .sigma. is set to
.sigma.=130, and the process then advances to the exit block.
As stated previously, when the process shown in FIG. 18 reaches the
exit block, p contains the pulserate, and .sigma. contains the
confidence factor. The confidence factor is a number indicating the
likelihood that the value of p accurately represents the actual
pulserate of the patient.
Transform Based Pulserate Detection
In accordance with another aspect of this invention, the pulserate
can be determined in the presence of FM and AM distortions by using
a pleth to pulserate transform (PPRT). FIG. 17 shows a schematic of
a signal processing system that implements a PPRT. In FIG. 17, a
time domain plethysmographic waveform f(t) is fed into an input
1701. The signal at the input 1701 is fed into a Fourier transform
block 1702 which forward transforms f(t) into the frequency domain.
An output of the block 1702 is expressed mathematically as
F(.omega.)={character pullout}[f(t)]. The output of the block 1702
is fed into a magnitude block 1703 which finds the magnitude of the
signal F(.omega.). An output of the magnitude block 1703, shown as
a plot 1713, is fed into a second forward Fourier transform block
1704 which transforms the signal .vertline.F(.omega.).vertline.
into a signal G(x) where G(x)={character
pullout}[.vertline.F(.omega.).vertline.] and G(x) is a complex
number. The output of the block 1704 is fed into a block 1705 which
extracts the real portion of G(x). The real portion of G(x) is then
fed into a 1/x mapping block 1706. An output of the mapping block
1706 is fed into a pulserate detector block 1707. A pulserate
output from the detector block 1706 is sent to a display 1708.
In an alternate embodiment, the magnitude block could be replaced
by a block which extracts the real portion of the waveform.
Likewise, the block 1705 which extracts the real portion of G(x)
could be replaced by a magnitude block which extracts
.vertline.G(x).vertline..
One skilled in the art will recognize that the output of the
magnitude block 1703 is merely the absolute value of the Fourier
transform of the plethysmographic wave f(t) on a point by point
basis. The graph 1713 shows this signal as a series of spectral
lines of varying amplitudes. In many cases, this spectrum will be
similar to that shown in FIG. 14B, and has a fundamental frequency
f.sub.0, and a series of harmonics f.sub.1 and f.sub.2 of various
amplitudes. As shown in FIG. 14B, any attempt at determining
pulserate merely by finding the largest spectral line will lead to
erroneous results. Further, the clean waveform of FIG. 14B, showing
a series of spectral peaks, will often be contaminated by AM
sidebands as shown in FIG. 15. Thus the fundamental periodic nature
of the heartbeat is not always readily apparent in the spectrum of
plot 1713. This is the reason for the second Fourier transform in
process block 1704.
The nature of the Fourier transform is to identify and quantify the
periodic nature of a function. If the waveform shown in the plot
1713 were in the time domain, rather than the frequency domain,
then the series of pulses (the spectral lines of the plot 1713)
would correspond to a periodic train of pulses, having a
fundamental frequency given by the pulse repetition frequency and
modulated by the spectrum of the individual pulses. Mathematically,
it does not matter that the waveform of the plot 1713 is not in the
time domain. The Fourier transform can still be applied, and it
will still produce a very strong spectral line corresponding to the
inherent periodicity, and corresponding component strength, of the
waveform.
Thus, the operation of the block 1704, in performing a forward
Fourier transform on a frequency domain waveform is mathematically
viable, and yields the desired data. The only unique ramification
of the fact that the transformed data is already in the frequency
domain rather than the time domain is the effect on the x axis. It
is well known to those skilled in the art, that the forward Fourier
transform maps the x axis into 1/x. This is most easily explained
by noting that, normally, one would transform f(t) into F(.omega.).
Since t=1/.omega. (to within a constant factor of 2.pi.) it is
clear that a 1/x mapping has occurred. In the present context, the
1/x mapping is undesirable because the data was already in the
frequency domain. Thus the mapping must be undone by the process
block 1706.
Once the waveform has been remapped in the process block 1707, it
is a simple matter to find the desired pulserate in the process
block 1707, because the pulserate will correspond to the largest
spectral peak. Again, this occurs because the second, Fourier
transform "identifies" the dominant periodicity (e.g., the dominant
string of harmonics) and collapses that periodicity into a single
spectral line. The pulserate detector 1707 merely searches for the
largest spectral peak and sends, to the display 1708, the frequency
that corresponds to the largest peak.
In yet another embodiment, the process block 1707 looks for the
existence of a spectral peak below 120 beats per minute. If a
spectral peak below 120 beats per minute is found, then the
frequency corresponding that peak is the pulserate. Of, on the
other hand, no spectral peak below 120 beats per minute is found,
then the process block 1707 finds the largest spectral peak in the
original fourier spectrum that exists at the output of the Fourier
transform block 1702. The pulserate is then the frequency
corresponding to the largest spectral peak at the output of the
Fourier transform block 1702.
In yet another embodiment, the ratio of the largest two peaks in
the PPRT waveform 1716 can be used to generate a confidence factor
that provides some indication of the accuracy of the computed
pulserate. In a preferred embodiment, a contrast ratio is computed
by dividing the magnitude of the largest peak in the PPRT waveform
1716 by the magnitude of the second largest peak in the PPRT
waveform 1716. A large contrast ratio corresponds to high
confidence that the computed pulserate is accurate. A contrast
ratio near unity corresponds to low confidence that the computed
pulserate is accurate.
Neural Network Embodiments
In yet another embodiment, much of the signal processing can be
accomplished by a neural network. One skilled in the art will
recognize that the signal processing associated with the removal of
motion artifacts involves non-linear and linear processes. The
frequency domain waveform scrubber 1260 and the time domain
waveform scrubber 1242 are both linear processes. However, the
calculation of .alpha. in FIG. 12 is a non-linear process, in, part
because it includes the ratio operation represented by the process
block 1230. The calculation of pulserate, either by the rule based
method, or the PPRT method both involve ratios and are thus
non-linear processes as well.
One skilled in the art will appreciate that other non-linear
filtering processes can be used. In particular, any of these
non-linear processes can be performed by a neural network as shown
in FIG. 19. FIG. 19 depicts a general hardware block diagram of a
pulse oximeter 299 that employs neural network processing. A sensor
300 has two light emitters 301 and 302 such as LED's. One LED 301
emitting light of red wavelengths and another LED 302 emitting
light of infrared wavelengths are placed adjacent a finger 310. A
photodetector 320, which produces an electrical signal
corresponding to the attenuated visible and infrared light energy
signals is, located near the LED's 301 and 302. The photodetector
320 is connected to front end analog signal conditioning circuity
330.
The front end analog signal conditioning circuit 330 has outputs
coupled to an analog to digital conversion circuit 332. The analog
to digital conversion circuit 332 has outputs coupled to a digital
signal processing and neural network signal extraction system 1934.
The signal processing system 1934 provides the desired parameters
as outputs for a display 336. Outputs for display are, for example,
blood oxygen saturation, heart rate, and a clean plethysmographic
waveform.
The signal processing system also provides an emitter current
control output 337 to a digital-to-analog converter circuit 338
which provides control information for a set of light emitter
drivers 340. The light emitter drivers 340 couple to the light
emitters 301, 302. The signal processing system 1934 also provides
a gain control output 343 for the front end analog signal
conditioning circuitry 330.
Additional Embodiments
While one embodiment of a physiological monitor incorporating a
processor of the present invention for determining a reference
signal for use in a waveform scrubber, to remove or derive primary
and secondary components from a physiological measurement has been
described in the form of a pulse oximeter, it will be obvious to
one skilled in the art that other types of physiological monitors
may also employ the above described techniques.
In particular, one skilled in the art will recognize that in all
cases, the Fourier transform disclosed above can be replaced by a
Fast Fourier Transform (FFT), a Chirp-Z Transform, a wavelet
transform, a discrete Fourier transform, or any other operation
that produces the same or similar result.
Furthermore, the signal processing techniques described in the
present invention may be used to compute the arterial and venous
blood oxygen saturations of a physiological system on a continuous
or nearly continuous time basis. These calculations may be
performed, regardless of whether or not the physiological system
undergoes voluntary motion.
Furthermore, it will be understood that transformations of measured
signals other than logarithmic conversion and that the
determination of a proportionality factor which allows removal or
derivation of the primary or secondary signal portions for
determination of a reference signal are possible. Additionally,
although the proportionality factor r has been described herein as
a ratio of a portion of a first signal to a portion of a second
signal, a similar proportionality constant determined as a ratio of
a portion of a second signal to a portion of a first signal could
equally well be utilized in the processor of the present invention.
In the latter case, a secondary reference signal would generally
resemble n'(t)=n.sub.b (t)-rn.sub.a (t).
One skilled in the art will realize that many different types of
physiological monitors may employ the teachings of the present
invention. Other types of physiological monitors include, but are
in not limited to, electro-cardiographs, blood pressure monitors,
blood constituent monitors (other than oxygen saturation) monitors,
capnographs, heart rate monitors, respiration monitors, or depth of
anesthesia monitors. Additionally, monitors which measure the
pressure and quantity of a substance within the body such as a
breathalyzer, a drug monitor, a cholesterol monitor, a glucose
monitor, a carbon dioxide monitor, a glucose monitor, or a carbon
monoxide monitor may also employ the above described
techniques.
Furthermore, one skilled in the art will recognize that many of the
signal processing techniques, and many of the filters disclosed
herein are classification techniques. Many of the classification
mechanisms herein involve classification of spectral lines and
ratios of various spectral lines. Other classification schemes are
possible within the spirit and scope of the invention.
Furthermore, one skilled in the art will realize that the above
described techniques of primary or secondary signal removal or
derivation from a composite signal including both primary and
secondary components can also be performed on electrocardiography
(ECG) signals which are derived from positions on the body which
are close and highly correlated to each other.
Furthermore, one skilled in the art will realize that the above
described techniques can also be performed on signals made up of
reflected energy, rather than transmitted energy. One skilled in
the art will also realize that a primary or secondary portion of a
measured signal of any type of energy, including but not limited to
sound energy, X-ray energy, gamma ray energy, or light energy can
be estimated by the techniques described above. Thus, one skilled
in the art will realize that the techniques of the present
invention can be applied in such monitors as those using ultrasound
where a signal is transmitted through a portion of the body and
reflected back from within the body back through this portion of
the body. Additionally, monitors such as echo-cardiographs may also
utilize the techniques of the present invention since they too rely
on transmission and reflection.
While the present invention has been described in terms of a
physiological monitor, one skilled in the art will realize that the
signal processing techniques of the present invention can be
applied in many areas, including but not limited to the processing,
of a physiological signal. The present invention may be applied in
any situation where a signal processor comprising a detector
receives a first signal which includes a first primary signal
portion and a first secondary signal portion and a second signal
which includes a second primary signal portion and a second
secondary signal portion. Thus, the signal processor of the present
invention is readily applicable to numerous signal processing
areas.
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